Real-Time Score Prediction using Machine Learning

Description

This project employs machine learning techniques to make real-time predictions of football match scores. By using Python and linear regression, the project processes match data and predicts the likelihood of a team winning, losing, or drawing. The dataset includes matches from the English Premier League, covering multiple seasons.

Tech Stack

• Python: Programming language used for implementing the project.
• Pandas: Data manipulation and analysis library.
• Scikit-learn: Machine learning library for Python.

File Structure

• .gitignore: Specifies files to be ignored by Git.
• LICENSE: License for the project.
• Prediction.ipynb: Jupyter notebook containing the prediction model and analysis.
• README.md: Project documentation.
• code.py: Python script for data preprocessing and model training.
• logo.jpeg: Project logo.
• matches.csv: Dataset containing match details.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

How to Run

1. Ensure you are in the project directory.
2. Run the Jupyter notebook:

   jupyter notebook Prediction.ipynb

3. Alternatively, you can run the Python script:

   python code.py

Usage

• Data Preprocessing: The code.py script reads the matches.csv file, preprocesses the data, and prepares it for model training.
• Model Training: The Random Forest classifier is trained on historical match data to predict future match outcomes.
• Prediction: The trained model predicts match outcomes for the test dataset.

Possible Output

The output of the model includes:

• Predicted match outcomes (win, lose, draw) for each match in the test dataset.
• Accuracy and precision scores to evaluate the model's performance.

Example

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score

# Load data
matches = pd.read_csv('matches.csv', index_col=0)

# Preprocess data
matches['date'] = pd.to_datetime(matches['date'])
matches['target'] = (matches['result'] == 'W').astype('int')
matches['venue_code'] = matches['venue'].astype('category').cat.codes
matches['opp_code'] = matches['opponent'].astype('category').cat.codes
matches['hour'] = matches['time'].str.replace(":.+", "", regex=True).astype('int')
matches['day_code'] = matches['date'].dt.dayofweek

# Train-test split
train = matches[matches['date'] < '2022-01-01']
test = matches[matches['date'] > '2022-01-01']

# Model training
predictors = ['venue_code', 'opp_code', 'hour', 'day_code']
rf = RandomForestClassifier(n_estimators=50, min_samples_split=10, random_state=1)
rf.fit(train[predictors], train['target'])

# Prediction
preds = rf.predict(test[predictors])

# Evaluation
precision = precision_score(test['target'], preds)
print(f'Precision: {precision:.2f}')

Delpoyment

Check this out

License

This project is licensed under the MIT License - see the LICENSE file for details.